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Updated: Jul 22, 2025
Development of a 68Gallium-Labeled D-Peptide PET Tracer for Imaging Programmed Death-Ligand 1 Expression
Published on: February 3, 2023
Probing the origins of programmed death ligand-1 inhibition by implementing machine learning-assisted sequential
Shruthy Kuttappan1,2, Ratul Bhowmik1, C Gopi Mohan3
1Bioinformatics and Computational Biology Lab, Amrita School for Nanosciences and Molecular Medicine, Amrita Vishwa Vidyapeetham, Kochi, Kerala State, 682 041, India.
Abstract:
PD-L1 is a key immunotarget involved in binding to its receptor PD-1. PD-L1/PD-1 interface blocking using antibodies (or small molecules) is the central area of interest for tumor suppression in various cancers. Blocking the PD-L1/PD-1 pathway in the tumor cells results in its immune activation and destruction, and thereby restoring the T-cell proliferation and cytokine production. The active binding site interface residues of PD-L1/PD-1 were experimentally known and proven by structural biology and site-directed mutagenesis studies. Structure-based molecular design technique was employed to identify the inhibitors for blocking the PD-L1/PD-1 interface. Nine hits to leads were identified from the SPECS small molecule database by machine learning, molecular docking, and molecular dynamics simulation techniques. Following this, a machine learning-assisted QSAR modeling approach was implemented using ChEMBL database to gain insights into the inhibitory potential of PD-L1 inhibitors and predict the activity of our previously screened nine hit molecules. The best leads identified in the present study bind strongly with the active sites of PD-L1/PD-1 interface residues, which include A121, M115, I116, S117, I54, Y56, D122, and Y123. These computational leads are considered promising molecules for further in vitro and in vivo analysis to be developed as potential PD-L1 checkpoint inhibitors to cure different types of cancers.
Insights
Researchers identified novel small molecules that block the PD-L1/PD-1 pathway, crucial for cancer immunotherapy. These potential PD-L1 checkpoint inhibitors show promise for developing new cancer treatments.
Area of Science:
- Immunology
- Computational Chemistry
- Drug Discovery
Background:
- Programmed death-ligand 1 (PD-L1) is a key immune checkpoint target involved in tumor immune evasion.
- Blocking the PD-L1/PD-1 interaction is a promising strategy for cancer immunotherapy, aiming to restore anti-tumor immune responses.
- Structural and mutagenesis studies have elucidated the PD-L1/PD-1 binding interface.
Purpose of the Study:
- To identify novel small molecules that inhibit the PD-L1/PD-1 interaction using structure-based molecular design.
- To computationally screen and evaluate potential inhibitors for their binding affinity and inhibitory potential.
Main Methods:
- Structure-based molecular design was employed to identify inhibitors targeting the PD-L1/PD-1 interface.
- Machine learning, molecular docking, and molecular dynamics simulations were used to screen the SPECS database, identifying nine hit molecules.
- Quantitative Structure-Activity Relationship (QSAR) modeling, aided by machine learning and the ChEMBL database, was used to predict the activity of the identified hit molecules.
Main Results:
- Nine potential small molecule inhibitors targeting the PD-L1/PD-1 interface were identified.
- The best lead compounds demonstrated strong binding to key interface residues, including A121, M115, I116, S117, I54, Y56, D122, and Y123.
- QSAR modeling provided insights into the inhibitory potential of these molecules.
Conclusions:
- The identified computational leads represent promising candidates for further preclinical evaluation (in vitro and in vivo).
- These molecules hold potential for development as novel PD-L1 checkpoint inhibitors to treat various cancers.
- This study highlights the efficacy of computational approaches in accelerating the discovery of targeted cancer therapeutics.
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